Patient motion during magnetic resonance imaging (MRI) data acquisition can lead to image artifacts which compromise the diagnostic quality of the resulting images. This is an important problem and has lead to a large number of methods which aim at reducing the impact of patient motion. Despite these past efforts, patient motion is still an important problem today; partly because increased SNR and fast scan methods of modern magnetic resonance (MR) scanners allow imaging at higher spatial resolution which makes the experiment more sensitive to motion, partly because the proposed methods are impractical in routine clinical use. This can be the case either because they require sensors which are too complicated and/or time consuming to connect, or because they prolong the scan duration excessively.
Other common limitations are restriction to certain anatomies/imaging sequences, negative impact on image contrast etc.
In “The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics),” Chapter 14.3 by Trevor Hastie, Robert Tibshirani, Jerome Friedman methods of cluster analysis are explained.